Dataset Information

Cross-validation in cryo-EM-based structural modeling.

ABSTRACT: Single-particle cryo-EM is a powerful approach to determine the structure of large macromolecules and assemblies thereof in many cases at subnanometer resolution. It has become popular to refine or flexibly fit atomic models into density maps derived from cryo-EM experiments. These density maps are typically significantly lower in resolution than electron density maps obtained from X-ray diffraction experiments, such that the number of parameters that need to be determined is much larger than the number of experimental observables. Overfitting and misinterpretation of the density, thus, become a serious problem. For diffraction data, a cross-validation approach was introduced almost 20 y ago; however, no such approach has been described yet for structure refinement against cryo-EM density maps, although the overfitting problem is, because of the lower resolution, significantly larger. We present a cross-validation approach for real-space refinement against cryo-EM density maps in analogy to cross-validation typically used in crystallography. Our approach is able to detect overfitting and allows for optimizing the choice of restraints used in the refinement. The approach is shown on three protein structures with simulated data and experimental data of the rotavirus double-layer particle. Because cross-validation requires splitting the dataset into at least two independent sets, we further present an approach to quantify correlations between the structure factor sets. This analysis is also helpful for other cross-validation applications, such as refinements against diffraction data or 3D reconstructions of cryo-EM density maps.

SUBMITTER: Falkner B

PROVIDER: S-EPMC3670386 | biostudies-literature | 2013 May

REPOSITORIES: biostudies-literature

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Publications

Cross-validation in cryo-EM-based structural modeling.

Falkner Benjamin B Schröder Gunnar F GF

Proceedings of the National Academy of Sciences of the United States of America 20130514 22

Single-particle cryo-EM is a powerful approach to determine the structure of large macromolecules and assemblies thereof in many cases at subnanometer resolution. It has become popular to refine or flexibly fit atomic models into density maps derived from cryo-EM experiments. These density maps are typically significantly lower in resolution than electron density maps obtained from X-ray diffraction experiments, such that the number of parameters that need to be determined is much larger than th ...[more]

PMID: 23674685

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Project description:MotivationCryo-electron microscopy (cryo-EM) is a widely used technology for ultrastructure determination, which constructs the 3D structures of protein and macromolecular complex from a set of 2D micrographs. However, limited by the electron beam dose, the micrographs in cryo-EM generally suffer from the extremely low signal-to-noise ratio (SNR), which hampers the efficiency and effectiveness of downstream analysis. Especially, the noise in cryo-EM is not simple additive or multiplicative noise whose statistical characteristics are quite different from the ones in natural image, extremely shackling the performance of conventional denoising methods.ResultsHere, we introduce the Noise-Transfer2Clean (NT2C), a denoising deep neural network (DNN) for cryo-EM to enhance image contrast and restore specimen signal, whose main idea is to improve the denoising performance by correctly learning the noise distribution of cryo-EM images and transferring the statistical nature of noise into the denoiser. Especially, to cope with the complex noise model in cryo-EM, we design a contrast-guided noise and signal re-weighted algorithm to achieve clean-noisy data synthesis and data augmentation, making our method authentically achieve signal restoration based on noise's true properties. Our work verifies the feasibility of denoising based on mining the complex cryo-EM noise patterns directly from the noise patches. Comprehensive experimental results on simulated datasets and real datasets show that NT2C achieved a notable improvement in image denoising, especially in background noise removal, compared with the commonly used methods. Moreover, a case study on the real dataset demonstrates that NT2C can greatly alleviate the obstacles caused by the SNR to particle picking and simplify the identifying of particles.Availabilityand implementationThe code is available at https://github.com/Lihongjia-ict/NoiseTransfer2Clean/.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Cross-validation in cryo-EM-based structural modeling.

Publications

Cross-validation in cryo-EM-based structural modeling.

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